Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/agentlas-desktop --skill scientific-visualizationgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/scientific-visualization)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/scientific-visualization"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/scientific-visualization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/scientific-visualization"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/scientific-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.03183 |
| Opus 5.5 | $0.00023 | $0.01273 |
| Sonnet 5.5 | $0.00012 | $0.00637 |
| Haiku 4.5 | $0.00006 | $0.00318 |
Grade A, and why
scientific-visualization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to scientific-visualization — 43 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Visualization
Build figures that preserve scientific meaning before optimizing appearance. Separate universal principles from dated publisher rules, preserve raw data and transformations, use color redundantly, and inspect delivered files rather than trusting plotting defaults.
Non-negotiable guardrails
- Never alter, hide, invent, or selectively enhance data to improve a figure.
- Preserve raw tables/images, exclusions, missing-value codes, analysis code, normalization, binning, image adjustments, and random seeds.
- Do not infer journal requirements. Identify the exact journal, article type, figure type, and submission phase; verify its live official guidance.
- Do not claim that a palette, DPI value, format, or automated report makes a figure accessible or journal-compliant.
- Do not silently connect missing observations, suppress inconvenient points, upsample images as if detail increased, or tune axes/dual axes to exaggerate a conclusion.
- Keep interactive and static outputs as distinct deliverables. Interactive hover is not a substitute for labels, alt text, keyboard access, an accessible data table, or a static fallback.
Read references/publication_guidelines.md for deceptive-encoding and integrity checks. Read references/journal_requirements.md only after the target and phase are known.
Workflow
1. Define the evidence and destination
Record:
- audience and medium: manuscript, web, slide, poster, supplement;
- exact publisher/journal, article type, submission phase, and intended final width;
- variable semantics, units, sample/replicate structure, missing/censored values;
- estimator and uncertainty definition;
- transformations: filtering, aggregation, normalization, smoothing, bins, image processing;
- source-data paths/identifiers and output provenance.
If requirements are not known, create a provisional general figure and label all publisher choices as pending verification.
2. Choose an honest encoding
Prefer position on a common scale. Before coding, check:
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/color_palettes.py 6.6 KB runs code
- assets/nature.mplstyle 1.6 KB
- assets/presentation.mplstyle 1.4 KB
- assets/publication.mplstyle 1.8 KB
- assets/publisher_profiles.json 9.6 KB
- references/color_palettes.md 8.9 KB
- references/journal_requirements.md 10 KB
- references/matplotlib_examples.md 11 KB
- references/publication_guidelines.md 12 KB
- references/sources.md 8.6 KB
- scripts/_common.py 4.2 KB runs code
- scripts/export_plan.py 16 KB runs code
- scripts/figure_export.py 22 KB runs code
- scripts/image_metadata.py 24 KB runs code
- scripts/palette_audit.py 11 KB runs code
- scripts/style_presets.py 16 KB runs code
- scripts/style_preview.py 7.6 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 303 lines · 58 tokens per session scan A f7c7f9a2350e
scientific-visualization is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 58 tokens to every session and 3,183 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to scientific-visualization, differing in 43 lines, and is treated as a copy.
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